JP2009296382A - Image processing system, image processing method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing system capable of compressing a stored image in accordance with an output environment. <P>SOLUTION: An image processing system includes: a storage section for storing an image; a compression deciding section for deciding whether or not to compress an image; and a compression section for compressing the image, in the case where the compression deciding section decides that the image is to be compressed, with compression strengths different for a featured region in the image and a region other than the featured region in the image. In the case where the compression deciding section decides that the image is to be compressed, the compression section may compress the image, in any region other than the featured region in the image, with a compression strength higher than that in the featured region in the image. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to an image processing system, an image processing method, and a program. The present invention particularly relates to an image processing system and an image processing method for processing an image, and a program for the image processing system.

A monitoring and recording device that records video data of a person entering and exiting a monitoring area is known (see, for example, Patent Document 1). This monitoring and recording device records video data that best matches a predetermined standard among the captured moving object videos in association with time information when the video was shot in the storage.
JP 2006-217070 A

  With the technique described in the above-mentioned Patent Document 1, captured images are accumulated in a storage. For this reason, for example, if most of the storage capacity is consumed by a video with a very low usage rate as a monitoring record such as a video taken a long time ago, the storage cannot be used efficiently. As described above, the technique described in Patent Literature 1 cannot compress a stored image in accordance with the output environment.

  In order to solve the above problems, according to a first aspect of the present invention, there is provided an image processing system, a storage unit that stores an image, a compression determination unit that determines whether or not to compress an image, and a compression determination When the unit determines that the image should be compressed, the image processing unit includes a compression unit that compresses the image with a different compression strength in a feature region in the image and a region other than the feature region in the image.

  According to a second aspect of the present invention, there is provided a method for determining whether to store an image, a compression determination step for determining whether or not to compress an image, and that the image should be compressed in the compression determination step. In this case, the image processing apparatus includes a compression stage that compresses the image with a different compression strength between a feature area in the image and an area other than the feature area in the image.

  According to a third aspect of the present invention, there is provided a program for an image processing system, in which a computer stores a image, a compression determination unit that determines whether the image should be compressed, and a compression determination unit When it is determined that the image should be compressed, the image is made to function as a compression unit that compresses the image with a different compression strength between the feature region in the image and the region other than the feature region in the image.

  The above summary of the invention does not enumerate all necessary features of the present invention. In addition, a sub-combination of these feature groups can also be an invention.

  Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention.

  FIG. 1 shows an example of an image processing system 10 according to an embodiment. The image processing system 10 can function as a monitoring system as described below.

  The image processing system 10 includes a plurality of imaging devices 100a to 100d (hereinafter collectively referred to as imaging devices 100) that capture the monitoring target space 150, an image processing device 120 that processes captured images captured by the imaging device 100, and communication. It includes a network 110, an image processing device 170, an image DB 175, and a plurality of display devices 180a-d (hereinafter collectively referred to as display devices 180). The image processing apparatus 170 is provided in a space 165 different from the monitoring target space 150. The display device 180 is provided in a space 160 different from the monitoring target space 150 and the space 165.

  The imaging device 100a includes an imaging unit 102a and a captured image compression unit 104a. The imaging unit 102a captures a plurality of captured images by continuously capturing the monitoring target space 150. The captured image obtained by the imaging unit 102a may be a RAW format captured image. The captured image compression unit 104a synchronizes the RAW format captured images captured by the imaging unit 102a, compresses a moving image including a plurality of captured images obtained by the synchronization by MPEG encoding or the like, and converts the moving image data. Generate. Thus, the imaging device 100a encodes the moving image obtained by imaging the monitoring target space 150 to generate moving image data. The imaging device 100a outputs the moving image data to the image processing device 120.

  Note that the imaging device 100b, the imaging device 100c, and the imaging device 100d have the same configuration as that of the imaging device 100a, and thus description of each component of the imaging device 100b, the imaging device 100c, and the imaging device 100d is omitted. In this way, the image processing device 120 acquires the moving image data generated by each of the plurality of imaging devices 100 from each of the plurality of imaging devices 100.

  The image processing device 120 acquires a moving image by decoding the moving image data acquired from the imaging device 100. The image processing apparatus 120 has a plurality of different types of features such as an area where a person 130 is imaged, an area where a moving body 140 such as a vehicle is imaged, etc., from each of a plurality of captured images included in the acquired moving image. The feature region of is detected. Then, the image processing apparatus 120 compresses the image of the feature area with an intensity according to the type of the feature, and compresses the image of the area other than the feature area with an intensity stronger than the compression intensity for compressing the image of each feature area. To do.

  Note that the image processing device 120 generates feature area information including information for specifying the feature area detected from the captured image. Then, the image processing apparatus 120 attaches the feature area information to the compressed moving image data and transmits the feature area information to the image processing apparatus 170 through the communication network 110.

  The image processing device 170 receives the compressed moving image data associated with the feature area information from the image processing device 120. Then, the image processing apparatus 170 stores the compressed moving image data in the image DB 175 in association with the feature area information associated with the compressed moving image data. The image DB 175 may store the compressed moving image data in a nonvolatile storage medium such as a hard disk. As described above, the image DB 175 stores the compressed captured image.

  In response to a request from the display device 180, the image processing device 170 reads the compressed moving image data and the feature area information from the image DB 175, and decompresses the read compressed moving image data using the feature region information for display. A moving image is generated and transmitted to the display device 180 through the communication network 110. At this time, the image processing apparatus 170 re-compresses the display moving image and transmits it to the display apparatus 180 according to the network bandwidth of the communication network 110 or the processing capability of the display apparatus 180. The display device 180 displays the moving image for display supplied from the image processing device 170.

  The feature area information is text data including the position of the feature area, the size of the feature area, the number of feature areas, identification information for identifying the captured image in which the feature area is detected, or the like, or compressed or encrypted into the text data. It may be data that has been processed. Then, the image processing apparatus 170 identifies captured images that satisfy various search conditions based on the position of the feature region, the size of the feature region, the number of feature regions, and the like included in the feature region information. Then, the image processing device 170 may decode the identified captured image and provide the decoded captured image to the display device 180.

  As described above, according to the image processing system 10, since the information indicating the feature region is stored in association with the moving image, it is possible to quickly search and cue a captured image group that matches a predetermined condition in the moving image. it can. Also, according to the image processing system 10, only a captured image group that meets a predetermined condition can be decoded, so that a partial moving image that meets the predetermined condition can be displayed promptly in response to a reproduction instruction.

  FIG. 2 shows an example of a block configuration of the image processing apparatus 120. The image processing apparatus 120 includes an image acquisition unit 250, a feature region detection unit 203, a compression control unit 210, a compression unit 240, an association processing unit 206, a compression determination unit 260, a condition determination unit 265, a condition acquisition unit 270, and an output allowable amount. An acquisition unit 283 and an output unit 207 are provided. The image acquisition unit 250 includes a compressed moving image acquisition unit 201 and a compressed moving image expansion unit 202.

  The compressed moving image acquisition unit 201 acquires a compressed moving image. Specifically, the compressed moving image acquisition unit 201 acquires encoded moving image data generated by the imaging device 100. The compressed moving image expansion unit 202 generates a plurality of captured images included in the moving image by expanding the moving image data acquired by the compressed moving image acquisition unit 201. Specifically, the compressed video decompression unit 202 decodes the encoded video data acquired by the compressed video acquisition unit 201 and extracts a plurality of captured images included in the video. Note that the captured image included in the moving image may be a frame image and a field image.

  The plurality of captured images obtained by the compressed moving image decompression unit 202 are supplied to the feature region detection unit 203 and the compression unit 240. The feature region detection unit 203 detects a feature region from a moving image including a plurality of captured images. Specifically, the feature region detection unit 203 detects a feature region from each of the plurality of captured images.

  For example, the feature region detection unit 203 detects an image region whose image content changes in a moving image as a feature region. Specifically, the feature region detection unit 203 may detect an image region including a moving object as a feature region. Note that the feature region detection unit 203 may detect a plurality of feature regions having different types of features from each of the plurality of captured images. Note that the type of feature may be an index of the type of object such as a person and a moving object. The type of the object may be determined based on the degree of coincidence of the shape of the object or the color of the object. As described above, the feature region detection unit 203 may detect a plurality of feature regions having different types of included objects from a plurality of captured images.

  For example, the feature region detection unit 203 extracts an object that matches a predetermined shape pattern with a matching degree equal to or higher than a predetermined matching degree from each of the plurality of picked-up images, and in the picked-up image including the extracted object. The region may be detected as a feature region having the same feature type. Note that a plurality of shape patterns may be determined for each type of feature. As an example of the shape pattern, a shape pattern of a human face can be exemplified. Different face patterns may be determined for each of a plurality of persons. Thereby, the feature region detection unit 203 can detect different regions each including a different person as different feature regions.

  The feature region detection unit 203 includes, in addition to the person's face, a part of the human body such as the head of the person or the hand of the person, or at least a part of a living body other than the human body. Can be detected as a feature region. The living body includes a specific tissue existing inside the living body such as a tumor tissue or a blood vessel inside the living body. In addition to the living body, the feature region detection unit 203 may detect, as the feature region, a region in which a card such as money, a cash card, a vehicle, or a license plate of the vehicle is captured.

  In addition to pattern matching by template matching or the like, the feature region detection unit 203 detects feature regions based on learning results by machine learning (for example, Adaboost) described in, for example, Japanese Patent Application Laid-Open No. 2007-188419. It can also be detected. For example, an image feature amount extracted from a predetermined subject image and an image feature amount extracted from a subject image other than the predetermined subject are extracted from the predetermined subject image. Learn the features of the image features. Then, the feature region detection unit 203 may detect a region from which an image feature amount having a feature that matches the learned feature is extracted as a feature region.

  Note that the feature region detection unit 203 may detect a feature region by the method described in Japanese Patent Application No. 2008-078641. For example, the feature region detection unit 203 includes an image group including a captured image and one or more thinned images by thinning out the captured image of the object detection target at a predetermined ratio, or by thinning out in stages at the predetermined ratio. Is generated. Then, the feature region detection unit 203 calculates the evaluation value by applying the first filter to the relatively small first image in the generated image group. Here, the first filter acts on a two-dimensionally expanding region on the image, thereby generating an evaluation value representing the probability that a specific type of object exists in the region. In addition, the first filter includes a plurality of filters each acting on a plurality of areas having different numbers of pixels corresponding to the area of the area on the image or different in stages at the predetermined ratio. It may be a filter that acts on a relatively narrow region of the filter group that includes the filter group. The feature area detection unit 203 extracts a primary candidate area from which an evaluation value exceeding a predetermined first threshold is obtained from the first image.

  Then, the feature region detection unit 203 is a region that is one step wider than the first filter in the filter group in a region corresponding to the primary candidate region in the second image that has one more pixel number than the first image. The evaluation value is calculated by applying the second filter that operates on the. Then, the feature region detection unit 203 extracts secondary candidate regions from which an evaluation value exceeding a predetermined second threshold is obtained.

  Here, the feature region detection unit 203 repeats the extraction process of extracting the candidate region by applying the plurality of filters that operate on the regions having different widths to the corresponding region of the image group. Go. At this time, the feature region detection unit 203 sequentially repeats an extraction process that operates a filter that operates on a relatively narrow area, and an extraction process that operates a filter that operates on a relatively wide area. Specifically, the feature region detection unit 203 operates a filter that operates on a relatively large image on a relatively large image from an extraction process that operates on a relatively small image on a relatively small image. Repeat sequentially for the extraction process. Then, the feature region detection unit 203 detects a specific type of object by repeating two or more extraction processes and finally extracting candidate regions. Then, the feature region detection unit 203 detects a region where the specific type of object exists as a feature region. As described above, the feature region detection unit 203 causes the filter to be limited to the region extracted in the immediately preceding process in the subsequent extraction process. Therefore, the presence / absence of the object is sequentially selected in each of the plurality of extraction processes, and the feature region can be detected with higher accuracy. In addition, since the feature area is roughly screened with a small-sized image, the feature area can be detected at a higher speed.

  The feature region detection unit 203 may detect the feature region by a method described in Japanese Patent Application No. 2008-078636. For example, the feature region detection unit 203 operates on a region of a predetermined area that spreads two-dimensionally with the captured image, and calculates a plurality of feature amounts that are different from each other among the contour and the inside of a specific type of object. A feature region is detected using a filter. Specifically, the feature region detection unit 203 calculates a plurality of feature amounts by causing the plurality of filters to act on regions of a predetermined area on the captured image that is the object detection target. Here, a correspondence relationship between each feature amount calculated by each of the plurality of filters and a primary evaluation value representing the probability of being a specific type of object is associated with the plurality of filters. The feature region detection unit 203 obtains each primary evaluation value corresponding to each calculated feature amount based on the correspondence relationship. Then, the feature area detection unit 203 obtains a secondary evaluation value representing the probability that a specific type of object exists in the area by integrating a plurality of primary evaluation values corresponding to the plurality of filters. Then, the feature region detection unit 203 compares the secondary evaluation value with a threshold value, and extracts a region with a high probability that a specific type of object exists beyond the threshold value. Thereby, the feature region detection unit 203 detects the region as a feature region where a specific type of object exists. As described above, the feature region detection unit 203 combines a plurality of filters that extract feature amounts representing various features inside and inside the object outline, so that, for example, the feature area detection unit 203 has higher accuracy than the extraction based only on the outline shape. Regions can be extracted.

  The feature region detection unit 203 may detect the feature region by a method in which the method described in Japanese Patent Application No. 2008-078636 and the method described in Japanese Patent Application No. 2008-078641 are combined. Specifically, the plurality of filters described in relation to the method described in Japanese Patent Application No. 2008-078636 have a plurality of areas in which the number of pixels is different at a predetermined ratio or stepwise at a predetermined ratio. A plurality of filters may be included for each area, each acting on each of the regions. Each of the filters may be associated with the correspondence relationship corresponding to each of the filters. Then, the feature area detection unit 203 thins out the captured image of the object detection target at a predetermined ratio, or thins out the image at a predetermined ratio step by step, thereby obtaining an image group including the captured image and one or more thinned images. Generate. The feature region detection unit 203 calculates a plurality of feature amounts by applying a plurality of first filters that act on a relatively narrow region to a relatively small first image in the image group. Then, the feature area detection unit 203 obtains each primary evaluation value corresponding to each calculated feature amount based on the correspondence relationship corresponding to each of the plurality of first filters. Then, the feature area detection unit 203 obtains a secondary evaluation value representing the probability that a specific type of object exists in the area by integrating a plurality of primary evaluation values. Then, the feature region detection unit 203 compares the obtained secondary evaluation value with the first threshold value, and extracts a primary candidate region that has a high probability that a specific type of object exists beyond the first threshold value. .

  In addition, the feature region detection unit 203 has one stage higher than the plurality of first filters in the region corresponding to the primary candidate region of the second image having one step higher number of pixels than the first image in the image group. A plurality of feature amounts are calculated by applying a plurality of second filters that operate over a wide area. Then, the feature region detection unit 203 obtains each primary evaluation value corresponding to each calculated feature amount based on the correspondence relationship corresponding to each of the plurality of second filters. Then, the feature region detection unit 203 integrates a plurality of primary evaluation values corresponding to the plurality of second filters, and thereby represents a probability that a specific type of object exists in the region corresponding to the primary candidate region. Obtain an evaluation value. Then, the feature region detection unit 203 compares the obtained secondary evaluation value with the second threshold value, and extracts a secondary candidate region that has a high probability that a specific type of object exists beyond the second threshold value. .

  Here, the feature region detection unit 203 repeats the extraction process of extracting the candidate region by applying the plurality of filters that operate on the regions having different widths to the corresponding region of the image group. Go. At this time, the feature region detection unit 203 sequentially repeats an extraction process that operates a filter that operates on a relatively narrow area, and an extraction process that operates a filter that operates on a relatively wide area. Specifically, the feature region detection unit 203 operates a filter that operates on a relatively large image on a relatively large image from an extraction process that operates on a relatively small image on a relatively small image. Repeat sequentially for the extraction process. Then, the feature region detection unit 203 detects a specific type of object by repeating two or more extraction processes and finally extracting candidate regions. Then, the feature region detection unit 203 detects a region where the specific type of object exists as a feature region.

  The feature region detection unit 203 may detect the feature region by the method described in Japanese Patent Application No. 2008-098600. For example, the feature region detection unit 203 detects a feature region from a plurality of captured images included in moving images captured by the plurality of imaging devices 100. As an example, it is assumed that the imaging device 100a and the imaging device 100b capture the same scene. For example, the imaging device 100a and the imaging device 100b can function as a stereo camera. In the following description, the first captured image captured by the image capturing apparatus 100a and the second captured image captured by the image capturing apparatus 100b are referred to as a pair image. The feature region detection unit 203 detects a specific type of object that is projected on the pair image from the pair image, and detects the detected region of the specific type of object as a feature region.

  For each of the first and second captured images regarded as a pair image, the feature region detection unit 203 extracts a region in which a specific type of object is imprinted on each captured image. Here, the feature area detection unit 203 may detect an area in which a specific type of object is imprinted with rough detection accuracy. The feature region detection unit 203 detects a specific type of object by detecting a pair of regions corresponding to each other from the extracted region on the first captured image and the region on the second captured image. To do. For example, the feature area detection unit 203 calculates the distance from the image of the paired area to the subject imaged in the area. The feature area detection unit 203 can detect a specific type of object based on the three-dimensional shape of the subject based on the distance to the subject.

  Here, when detecting a pair of regions corresponding to each other, the feature region detection unit 203 detects a region in which a specific type of object detected from the first and second captured images considered as a pair image is imprinted. Divide into multiple sub-regions. Then, the feature region detection unit 203 calculates a vector over a plurality of subregions of the feature amount characterizing the partial image projected in each subregion. Here, as the feature amount, a pixel value can be exemplified. Moreover, as a vector over a plurality of sub-regions, a gradient vector (for example, a pixel value gradient vector) can be exemplified. Then, the feature area detection unit 203 calculates a logical distance between the calculated area vector on the first image and the vector on the second image. The feature region detection unit 203 detects a pair of regions whose logical distance is smaller than a predetermined value as a pair of regions corresponding to each other. As the logical distance, the square root of the sum of squares with respect to the difference between the components forming the vector can be exemplified. In this way, the feature region detection unit 203 can extract a pair of corresponding regions from the pair image with high accuracy, and thus can calculate the distance to the subject with high accuracy. Therefore, the feature region detection unit 203 can recognize the three-dimensional shape of the subject with high accuracy, and as a result, can detect a specific type of object with higher accuracy.

  The feature region detection unit 203 may detect the feature region by the method described in Japanese Patent Application No. 2008-091562. For example, the feature region detection unit 203 obtains a similar subject shape similar to a specific type of subject from each of the plurality of captured images included in the moving image, the size of the similar subject shape, and the position on the angle of view of the imaging device 100. Extract with information. The position information on the angle of view can be exemplified by the position on the image area in the captured image. Then, the feature region detection unit 203 determines whether or not the extracted subject of the similar subject shape is a specific type of subject, and extracts the specific type of subject. For example, the feature region detection unit 203 counts the number of detected similar subject shapes classified into the same size from a predetermined search region around the similar subject shape, and the count value is If the threshold is equal to or greater than the threshold, the subject having the similar subject shape may be extracted as a specific type of subject. Then, the feature region detection unit 203 may detect a region including a specific type of subject as a feature region. For this reason, the feature region detection unit 203 can detect a subject having a similar subject shape in an image region in which subjects having a size close to a predetermined size are concentrated and detected as a specific type of subject. Then, it is not necessary to detect a subject having a similar subject shape in a region other than the image region as a specific type of subject. For this reason, it is possible to reduce the probability of erroneously detecting a subject having a similar subject shape in a region other than the image region as a specific type of subject.

  Note that when the imaging apparatus 100 can capture an image with a variable angle of view, the position information on the angle of view can be exemplified by the imaging direction of the imaging apparatus 100 and the position on the captured image. In addition, when a plurality of imaging devices 100 can capture a continuous scene that is wider than the subject captured by one imaging device 100, the positional information on the angle of view includes a plurality of imaging devices. The respective imaging directions of 100 and the positions on the captured image captured by each of the plurality of imaging devices 100 can be exemplified.

  The feature area detection unit 203 can detect an area in which a predetermined subject is imaged as a feature area. Note that the feature region detection unit 203 may detect an ROI having an arbitrary shape including a rectangle.

  As described above, the feature region detection unit 203 detects a plurality of feature regions from a plurality of captured images included in each of the plurality of moving images. Then, the feature region detection unit 203 supplies information indicating the detected feature region to the compression control unit 210. Note that the information indicating the feature area includes coordinate information of the feature area indicating the position of the feature area, type information indicating the type of the feature area, and information for identifying the moving image in which the feature area is detected.

  The compression control unit 210 controls the compression process of the moving image by the compression unit 240 based on the information indicating the feature region acquired from the feature region detection unit 203. The compression unit 240 compresses the captured image with different intensities between the feature region in the captured image and the region other than the feature region in the captured image as described below under the control of the compression control unit 210. For example, the compression unit 240 compresses the captured image by reducing the resolution of the region other than the feature region in the captured image included in the moving image from the feature region. As described above, the compression unit 240 compresses each image area in the captured image with an intensity corresponding to the importance of the image area.

  Note that when the feature region detection unit 203 detects a plurality of feature regions, the compression unit 240 may compress the images of the plurality of feature regions in the captured image with an intensity corresponding to the feature type of each feature region. For example, the compression unit 240 may reduce the resolution of the images of the plurality of feature regions in the captured image to a resolution determined according to the feature type of the feature region.

  The association processing unit 206 associates information specifying the feature area detected from the captured image with the captured image. Specifically, the association processing unit 206 associates information for specifying a feature area detected from a captured image with a compressed moving image including the captured image as a moving image constituent image. Then, the output unit 207 outputs the compressed moving image data associated with the information specifying the feature region by the association processing unit 206 to the image processing apparatus 170.

  The image processing device 170 specifies a feature region in the captured moving image data from the plurality of captured images compressed by the compression unit 240 included in the compressed moving image in the captured moving image data output from the output unit 207. Get along with. Then, the image processing apparatus 170 stores the plurality of compressed captured images in the image DB 175 in association with information for specifying the feature region. Note that the plurality of captured images compressed by the compression unit 240 included in the captured moving image output from the output unit 207 may be an example of a compressed image in the present invention.

  Note that the image processing device 170 acquires the remaining storage capacity, which is a storage capacity in which the image DB 175 can further store data, and transmits it to the image processing device 120. The output allowable amount acquisition unit 283 acquires the remaining storage capacity transmitted from the image processing apparatus 170. In addition, the condition acquisition unit 270 acquires a condition regarding the remaining storage amount that should be satisfied when it is determined that the captured image should be further compressed. Then, the condition determination unit 265 determines whether or not the remaining storage amount acquired by the output allowable amount acquisition unit 283 satisfies the condition regarding the remaining storage amount acquired by the condition acquisition unit 270.

  When the condition determination unit 265 determines that the remaining storage amount acquired by the output allowable amount acquisition unit 283 satisfies the condition regarding the remaining storage amount acquired by the condition acquisition unit 270, the compression determination unit 260 It is determined that a plurality of captured images are further compressed. It should be noted that the compression determination unit 260 has a plurality of cases when the condition determination unit 265 determines that the condition is satisfied, compared to when the condition determination unit 265 determines that the condition is not satisfied. It may be determined that the captured image is further compressed with a stronger intensity. When the compression determination unit 260 determines that the plurality of captured images are to be further compressed, the compression control unit 210 causes the compression unit 240 to further compress the plurality of captured images. When the compression determination unit 260 determines that the captured image should be further compressed, the compression unit 240 compresses the captured image with different compression strengths in the feature region in the captured image and the region other than the feature region in the captured image. To do.

  Note that the compression determination unit 260 may determine whether to compress each of a plurality of captured images included in a plurality of moving images captured by each of the plurality of imaging devices 100. Then, when the compression determination unit 260 determines that at least one captured image should be compressed, the compression unit 240 at least one feature region of the plurality of feature regions in the captured image included in at least one moving image. The compression determination unit 260 may compress the image with a strength corresponding to the determination result determined for each of the plurality of captured images. Thereby, according to the determination result with respect to the moving image imaged by the several different imaging device 100, the compression rate with respect to the moving image imaged by the 1 or more imaging device 100 can be controlled. In addition, the compression rate can be controlled for each of the plurality of feature regions in the captured image according to the determination result for the moving images captured by the plurality of different imaging devices 100. For this reason, it is possible to finely control the image quality of a moving image captured by a plurality of different imaging devices 100 for each region according to the situation. The determination result by the compression determination unit 260 can be exemplified as to whether or not the condition acquired by the condition acquisition unit 270 is satisfied or the degree of conformity to the condition. In addition, when there are a plurality of different conditions, examples of the determination result by the compression determination unit 260 include a combination of satisfied conditions or a degree of fitness for each of the plurality of conditions.

  Thus, the condition acquisition unit 270 acquires a condition that should be satisfied when it is determined that the captured image should be further compressed. For example, the condition acquisition unit 270 acquires a condition relating to an output allowable amount that can be output to an output destination to which a captured image is output, which should be satisfied when it is determined that the captured image should be further compressed. Then, the condition determination unit 265 determines whether the output allowable amount that can be output to the output destination where the captured image is output satisfies the condition acquired by the condition acquisition unit 270. Then, when the condition determining unit 265 determines that the permissible output amount satisfies the condition, the compression determining unit 260 determines that the image should be further compressed. Then, when the compression determination unit 260 determines that the captured image should be further compressed, the compression unit 240 compresses the captured image in a feature area in the captured image and a region other than the feature area in the captured image. Compress further.

  In addition to the remaining storage capacity of the image DB 175, the allowable output amount is the network bandwidth that the image processing device 120 can output to the communication network 110, and the image processing device 170 can process per unit time. The amount of data can be exemplified. The operation of the output allowable amount acquisition unit 283, the condition acquisition unit 270, the condition determination unit 265, and the compression determination unit 260, and the operation of the compression unit 240 when the compression determination unit 260 determines that the captured image is further compressed. Will be described in more detail later in connection with the operation of the image processing apparatus 170.

  FIG. 3 shows an example of a block configuration of the compression unit 240. The compression unit 240 includes an image dividing unit 232, a plurality of fixed value conversion units 234a-c (hereinafter may be collectively referred to as a fixed value conversion unit 234), and a plurality of compression processing units 236a-d (hereinafter referred to as compression processing). Part 236 in some cases).

  The image dividing unit 232 acquires a plurality of captured images from the image acquisition unit 250. Then, the image dividing unit 232 divides the plurality of captured images into a feature region and a background region other than the feature region. Specifically, the image dividing unit 232 divides the plurality of captured images into each of a plurality of feature areas and a background area other than the feature areas. As described above, the image dividing unit 232 divides each of the plurality of captured images into the feature region and the background region. Then, the compression processing unit 236 compresses the feature region image, which is the feature region image, and the background region image, which is the background region image, with different strengths. Specifically, the compression processing unit 236 compresses a feature area moving image including a plurality of feature area images and a background area moving image including a plurality of background area images with different strengths.

  Specifically, the image dividing unit 232 generates a feature area moving image for each of a plurality of feature types by dividing a plurality of captured images. Then, the fixed value unit 234 fixes the pixel values of the regions other than the feature regions of the respective feature types for each of the feature region images included in the plurality of feature region moving images generated for each feature type. To do. Specifically, the fixed value converting unit 234 sets pixel values in regions other than the feature region to predetermined pixel values. Then, the compression processing unit 236 compresses a plurality of feature area moving images for each feature type. For example, the compression processing unit 236 performs MPEG compression on a plurality of feature area moving images for each feature type.

  The fixed value unit 234a, the fixed value unit 234b, and the fixed value unit 234c are a feature region moving image of the first feature type, a feature region moving image of the second feature type, and a third feature type, respectively. The feature area video of is fixed. Then, the compression processing unit 236a, the compression processing unit 236b, and the compression processing unit 236c are the feature region moving image of the first feature type, the feature region moving image of the second feature type, and the feature of the third feature type. Compress area video.

  Note that the compression processing units 236a-c compress the feature area moving image with a predetermined strength according to the type of feature. For example, the compression processing unit 236 may convert the feature region moving image into a moving image having a different resolution determined in advance according to the feature type of the feature region, and compress the converted feature region moving image. In addition, the compression processing unit 236 may compress the feature region moving image with different quantization parameters determined in advance according to the feature type when compressing the feature region moving image by MPEG encoding.

  The compression processing unit 236d compresses the background area moving image. It should be noted that the compression processing unit 236d may compress the background area moving image with a strength higher than the strength of any of the compression processing units 236a-c. The feature area moving image and the background area moving image compressed by the compression processing unit 236 are supplied to the association processing unit 206.

  Since areas other than the feature area are fixed values by the fixed value converting unit 234, when the compression processing unit 236 performs predictive encoding using MPEG encoding or the like, an image between the predicted image in the area other than the feature area is displayed. Can be significantly reduced. For this reason, the compression unit 240 can compress the feature region moving image with a higher compression rate.

  In this figure, each of a plurality of compression processing units 236 included in the compression unit 240 compresses a plurality of feature region images and a background region image. However, in another embodiment, the compression unit 240 performs one compression process. A single compression processing unit 236 may compress a plurality of feature region images and background region images with different intensities. For example, a plurality of feature region images and a background region image are sequentially supplied to one compression processing unit 236 in a time-sharing manner, and the one compression processing unit 236 differs from the plurality of feature region images and the background region image. You may compress sequentially by intensity.

  In addition, the one compression processing unit 236 quantizes the image information of the plurality of feature regions and the image information of the background region with different quantization coefficients, respectively, thereby converting the images of the plurality of feature regions and the images of the background region. They may be compressed with different strengths. Also, an image obtained by converting a plurality of feature region images and a background region image into images of different image quality is supplied to one compression processing unit 236, and the one compression processing unit 236 includes a plurality of feature region images and Each image in the background area may be compressed. Further, as described above, one compression processing unit 236 quantizes with a different quantization coefficient for each region, or one compression processing unit 236 compresses an image converted into a different image quality for each region. The compression processing unit 236 may compress the entire one image, or may compress each of the images divided by the image dividing unit 232 as described with reference to FIG. When one compression processing unit 236 compresses the entire one image, the dividing process by the image dividing unit 232 and the fixed value processing by the fixed value converting unit 234 do not have to be performed. The image dividing unit 232 and the fixed value converting unit 234 may not be provided.

  FIG. 4 shows an example of a block configuration of the image processing apparatus 170. The image processing apparatus 170 includes a compressed image acquisition unit 301, an association analysis unit 302, an expansion control unit 310, an expansion unit 320, a composition unit 330, a compression determination unit 360, a condition determination unit 365, a condition acquisition unit 370, and a determination information acquisition unit. 380 and a compression unit 340.

  The decompression unit 320 includes a plurality of decoders 322a-d (hereinafter collectively referred to as decoders 322). The determination information acquisition unit 380 includes a storage period acquisition unit 381, a remaining storage amount acquisition unit 382, an output allowable amount acquisition unit 383, and an authority acquisition unit 384. Hereinafter, functions and operations of each component of the image processing apparatus 170 when the moving image stored in the image DB 175 is further compressed will be described.

  The image acquisition unit 301 acquires the compressed moving image compressed by the compression unit 240. Specifically, the image acquisition unit 301 acquires a compressed moving image including a plurality of feature area moving images and a background area moving image. More specifically, the image acquisition unit 301 acquires a compressed moving image with feature area information attached thereto.

  The association analysis unit 302 separates the compressed moving image acquired from the image DB 175 into a plurality of feature region moving images, background region moving images, and feature region information, and supplies the plurality of feature region moving images and background region moving images to the decompressing unit 320. To do. The association analysis unit 302 also analyzes the feature region information and supplies the feature region position and the feature type to the extension control unit 310 and the composition unit 330.

  The decompression control unit 310 controls the decompression processing by the decompression unit 320 according to the position of the feature region and the feature type acquired from the association analysis unit 302. For example, the decompression control unit 310 decompresses each region of the moving image indicated by the compressed moving image to the decompressing unit 320 according to a compression method in which the compression unit 240 compresses each region of the moving image according to the position of the feature region and the type of feature. Let

  The decoder 322 decodes one of the plurality of encoded characteristic area moving images and background area moving images. Specifically, the decoder 322a, the decoder 322b, the decoder 322c, and the decoder 322d decode the first feature region moving image, the second feature region moving image, the third feature region moving image, and the background region moving image, respectively.

  The synthesizing unit 330 synthesizes a plurality of feature area moving images and background area moving images expanded by the expanding unit 320 to generate one moving image. Specifically, the synthesizing unit 330 generates one moving image by combining the image of the feature region on the captured image included in the plurality of feature region moving images with the captured image included in the background region moving image. The composition unit 330 supplies the generated moving image to the compression unit 340.

  The compression unit 340 uses the feature region information acquired from the association analysis unit 302 to compress the moving image obtained by the synthesis unit 330. For example, each of the plurality of captured images included in the moving image obtained by combining by the combining unit 330 is compressed with different compression strengths in the feature region in the captured image and the region other than the feature region in the captured image.

  Specifically, when the compression determination unit 360 determines that the captured image should be further compressed, the compression unit 340 compresses the region other than the feature region in the captured image with a stronger compression strength than the feature region in the captured image. To do. Note that the compression unit 340 can specify the position of the feature region based on the feature region information. Therefore, the compression unit 340 is stronger than the feature region indicated by the information stored in the image DB 175 in association with the captured image in a region other than the feature region indicated by the information stored in the image DB 175 in association with the captured image. The captured image can be compressed with the compression strength.

  Note that the compression unit 340 may compress each region of the plurality of feature regions in each of the plurality of captured images with a compression strength corresponding to the feature type of the feature region. For example, the compression unit 340 may compress the captured image by reducing the image quality of each of the plurality of feature regions in each of the plurality of captured images to an image quality according to the feature type of the feature region.

  Note that, as described above, the image DB 175 associates information indicating each of the plurality of feature areas with each of the plurality of feature areas, and each of the plurality of captured images is compressed with a stronger compression strength than the area other than the feature area. I remember it. In this case, the compression unit 340 may compress the captured image by further reducing the image quality in regions other than the feature region than the image quality in the plurality of feature regions. Further, the image DB 175 stores a plurality of captured images that are compressed with a compression strength corresponding to the type of feature in each of the plurality of feature regions in association with information indicating each of the plurality of feature regions. Yes. In this case, the compression unit 340 may compress the captured image by reducing the image quality of each of the plurality of feature regions in each of the plurality of captured images by a reduction amount corresponding to the feature type of the feature region.

  More specifically, the compression unit 340 may compress the captured image by further reducing the resolution in a region other than the feature region than the resolution in the plurality of feature regions. The compression unit 340 may compress the captured image by further reducing the number of colors in a region other than the feature region than the number of colors in the plurality of feature regions. In addition, the compression unit 340 may compress the captured image by further reducing the number of gradations in a region other than the feature region than the number of gradations in the plurality of feature regions.

  The compression unit 340 may compress the captured image by further reducing the spatial frequency component in the higher frequency region in the region other than the feature region than the plurality of feature regions. The compression unit 340 controls the quantization parameter for quantizing the image data of a plurality of feature regions and regions other than the feature regions, thereby further reducing spatial frequency components in regions other than the feature regions than the plurality of feature regions. You can do it. The compression unit 340 may cut a spatial frequency component in a frequency region higher than a predetermined frequency region in a region other than the feature region in the compressed captured image acquired by the image acquisition unit 301. As described above, the compression unit 340 can compress the captured image by further reducing the code amount in a region other than the feature region than the code amount in the plurality of feature regions.

  The moving image compressed by the compression unit 340 is supplied to the image DB 175 or the display device 180. The image DB 175 stores the moving image compressed by the compression unit 340 in association with the feature area information. The display device 180 displays the moving image compressed by the compression unit 340.

  Note that the image processing apparatus 170 may compress the moving image by the above-described operation when a predetermined condition is satisfied. Specifically, the compression determination unit 360 determines whether or not to further compress a plurality of captured images included as moving image constituent images in the compressed moving image stored in the image DB 175. Then, when the compression determination unit 360 determines that the captured image should be further compressed, the compression unit 340 compresses the captured image in a feature area in the captured image and an area other than the feature area in the captured image. Compress with.

  More specifically, the condition acquisition unit 370 acquires a condition that should be satisfied when the compression determination unit 360 determines that the captured image should be further compressed. Then, when the condition determination unit 365 determines that the condition acquired by the condition acquisition unit 370 is satisfied, the condition determination unit 365 determines that the captured image should be further compressed. As described above, the condition determining unit 365 determines whether or not a condition that should be satisfied when the compression determining unit 360 determines that the captured image should be further compressed is satisfied. Then, when the condition determination unit 365 determines that the condition is satisfied, the compression determination unit 360 determines that the captured image should be further compressed.

  Note that the determination information acquisition unit 380 acquires information used by the condition determination unit 365 to determine whether the condition is satisfied. Specifically, the storage period acquisition unit 381 acquires the length of time during which the image DB 175 stores captured images. Then, the condition determination unit 365 determines whether or not a condition indicating that the time length acquired by the storage period acquisition unit 381 is longer than a predetermined value is satisfied. Then, the compression determination unit 360 determines that the captured image should be further compressed when the condition determination unit 365 determines that the time length of the period in which the image DB 175 stores the captured image satisfies the condition. To do.

  Note that the time length of the period in which the image DB 175 stores the captured image may be the length of time that has elapsed since the image capturing apparatus 100 captured the image. Further, the length of time during which the image DB 175 stores the captured image may be the length of time that has elapsed since the time when the image DB 175 was last supplied to the display device 180.

  Further, the remaining storage capacity acquisition unit 382 acquires the remaining storage capacity that the image DB 175 can further store. Then, the condition determination unit 365 determines whether a condition indicating that the remaining storage amount acquired by the remaining storage amount acquisition unit 382 is smaller than a predetermined value is satisfied. Then, when the condition determination unit 365 determines that the remaining storage capacity satisfies the condition, the compression determination unit 360 determines that the captured image should be further compressed.

  The allowable output amount acquisition unit 383 acquires an allowable output amount that can be output to an output destination where the captured image is output. Then, the condition determining unit 365 determines whether a condition indicating that the output allowable amount acquired by the output allowable amount acquiring unit 383 is smaller than a predetermined value is satisfied. Then, when the condition determining unit 365 determines that the output allowable amount satisfies the condition, the compression determining unit 360 determines that the captured image should be further compressed.

  The output destination to which the captured image is output can be exemplified by the communication network 110 and the display device 180. Further, as an output allowable amount that can be output to the output destination, when the output destination is the communication network 110, the network bandwidth of the communication network 110 can be exemplified, and the output destination is the display device 180. In this case, the amount of data per unit time that can be processed by the display device 180, the remaining buffer capacity of the display device 180, or the remaining buffer capacity of the image processing device 170 prepared for the display device 180 is exemplified. be able to.

  The network bandwidth can be exemplified by a bandwidth determined according to a contracted bandwidth and a network congestion amount. In addition, the amount of network congestion can be used as an index for the amount of packet loss caused by communication packets lost with the display device 180 and the amount of block omission in a moving image received by the image processing device 170. Note that the image processing apparatus 170 may acquire the packet loss amount lost in the communication packet with the display device 180 or the block omission amount in the moving image received by the image processing device 170 by feedback from the display device 180. Good.

  In addition, the authority acquisition unit 384 acquires a browsing authority to browse a moving image output from the image processing apparatus 170 to the display apparatus 180. Then, the condition determination unit 365 determines whether or not a condition indicating that the viewing authority acquired by the authority acquisition unit 384 is smaller than a predetermined value is satisfied. Then, the compression determination unit 360 determines that the captured image should be further compressed when the condition determination unit 365 determines that the viewing authority satisfies the condition. Note that the viewing authority may be the authority of the viewer who browses the video displayed on the display device 180. Further, the viewing authority may be an authority previously assigned to each of the plurality of display devices 180. Note that the compression unit 340 may compress the captured image by reducing the image quality of each of the plurality of feature regions in each of the plurality of captured images to an image quality according to the type of feature of the feature region and the viewing authority. .

  The image DB 175 stores a plurality of moving images captured by each of the plurality of imaging devices 100. In this case, the compression determination unit 360 may determine whether or not each of the plurality of captured images included in the plurality of moving images stored in the image DB 175 should be compressed. Then, the compression unit 340, when the compression determination unit 360 determines that at least one captured image should be compressed, at least one feature region of the plurality of feature regions in the captured image included in at least one moving image. The compression determination unit 360 may perform compression with an intensity corresponding to the determination result determined for each of the plurality of captured images. Note that the determination result by the compression determination unit 360 can exemplify whether the condition acquired by the condition acquisition unit 370 is satisfied or the degree of conformity to the condition. In addition, when there are a plurality of different conditions, examples of the determination result by the compression determination unit 360 include a combination of satisfied conditions or a degree of fitness for each of the plurality of conditions.

  In connection with FIG. 2, the operations of the compression determination unit 260, the condition determination unit 265, the condition acquisition unit 270, and the output allowable amount acquisition unit 283, and the compression determination unit 260 have determined that the captured image is further compressed. The outline of the operation of the compression unit 240 in this case has been described, but the compression determination unit 260, the condition determination unit 265, the condition acquisition unit 270, and the output allowable amount acquisition unit 283 are respectively the compression determination unit 360, the condition determination unit 365, and the condition The acquisition unit 370 and the allowable output amount acquisition unit 383 can operate in the same manner. Needless to say, the image processing system 10 may include a single image processing apparatus having constituent elements obtained by combining the constituent elements of the image processing apparatus 120 and the constituent elements of the image processing apparatus 170.

  FIG. 5 shows an example of another block configuration of the compression unit 240. The compression unit 240 in this configuration compresses a plurality of captured images by a spatial scalable encoding process corresponding to the type of feature.

  The compression unit 240 in this configuration includes an image quality conversion unit 510, a difference processing unit 520, and an encoding unit 530. The difference processing unit 520 includes a plurality of inter-layer difference processing units 522a-d (hereinafter collectively referred to as inter-layer difference processing units 522). Encoding section 530 includes a plurality of encoders 532a-d (hereinafter collectively referred to as encoders 532).

  The image quality conversion unit 510 acquires a plurality of captured images from the image acquisition unit 250. In addition, the image quality conversion unit 510 acquires information specifying the feature region detected by the feature region detection unit 203 and information specifying the type of feature in the feature region. Then, the image quality conversion unit 510 duplicates the captured image, and generates captured images of the number of types of features in the feature area. Then, the image quality conversion unit 510 converts the generated captured image into an image having a resolution corresponding to the type of feature.

  For example, the image quality conversion unit 510 has a captured image converted to a resolution corresponding to the background area (hereinafter referred to as a low resolution image), and a captured image converted to the first resolution corresponding to the type of the first feature ( Called a first resolution image), a captured image converted to a second resolution corresponding to the second feature type (referred to as a second resolution image), and a third resolution corresponding to the third feature type. A captured image (referred to as a third resolution image) converted to is generated. Here, it is assumed that the first resolution image has a higher resolution than the low resolution image, the second resolution image has a higher resolution than the first resolution image, and the third resolution image has a higher resolution than the second resolution image.

  Then, the image quality conversion unit 510 converts the low resolution image, the first resolution image, the second resolution image, and the third resolution image into the inter-layer difference processing unit 522d, the inter-layer difference processing unit 522a, and the inter-layer difference processing unit 522b, respectively. And to the inter-layer difference processing unit 522c. The image quality conversion unit 510 supplies a moving image to each of the inter-layer difference processing units 522 by performing the above-described image quality conversion processing on each of the plurality of captured images.

  Note that the image quality conversion unit 510 may convert the frame rate of the moving image supplied to each of the inter-layer difference processing unit 522 in accordance with the feature type of the feature region. For example, the image quality conversion unit 510 may supply, to the inter-layer difference processing unit 522d, a moving image having a lower frame rate than the moving image supplied to the inter-layer difference processing unit 522a. In addition, the image quality conversion unit 510 may supply a moving image having a lower frame rate than the moving image supplied to the inter-layer difference processing unit 522b to the inter-layer difference processing unit 522a, and a frame lower than the moving image supplied to the inter-layer difference processing unit 522c. The rate movie may be supplied to the inter-tier difference processing unit 522b. Note that the image quality conversion unit 510 may convert the frame rate of the moving image supplied to the inter-layer difference processing unit 522 by thinning out the captured image according to the feature type of the feature region. As described above, in the compression unit 240, the position of the pixel to be compressed is temporally shifted, but the pixel to be compressed may be selected by the image quality conversion unit 510.

  The inter-layer difference processing unit 522d and the encoder 532d predictively encode a background area moving image including a plurality of low-resolution images. Specifically, the inter-layer difference processing unit 522 generates a difference image from a predicted image generated from another low-resolution image. Then, the encoder 532d quantizes the transform coefficient obtained by converting the difference image into a spatial frequency component, and encodes the quantized transform coefficient by entropy coding or the like. Note that such predictive encoding processing may be performed for each partial region of the low-resolution image.

  Further, the inter-layer difference processing unit 522a predictively encodes the first feature region moving image including the plurality of first resolution images supplied from the image quality conversion unit 510. Similarly, the inter-layer difference processing unit 522b and the inter-layer difference processing unit 522c each predictively encode a second feature area moving image including a plurality of second resolution images and a third feature area moving image including a plurality of third resolution images. To do. Hereinafter, specific operations of the inter-layer difference processing unit 522a and the encoder 532a will be described.

  The inter-layer difference processing unit 522a decodes the first resolution image encoded by the encoder 532d, and expands the decoded image to an image having the same resolution as the first resolution. Then, the inter-layer difference processing unit 522a generates a difference image between the enlarged image and the low resolution image. At this time, the inter-layer difference processing unit 522a sets the difference value in the background area to zero. Then, the encoder 532a encodes the difference image in the same manner as the encoder 532d. Note that the encoding processing by the inter-layer difference processing unit 522a and the encoder 532a may be performed for each partial region of the first resolution image.

  Note that, when the first resolution image is encoded, the inter-layer difference processing unit 522a calculates the code amount predicted when the difference image with the low resolution image is encoded, and the other first resolution image. The amount of code predicted when the difference image between the generated prediction image and the prediction image is encoded is compared. In the case where the latter code amount is smaller, the inter-layer difference processing unit 522a generates a difference image from the predicted image generated from the other first resolution image. In addition, the inter-layer difference processing unit 522a, when it is predicted that the code amount is smaller when encoded without taking the difference from the low-resolution image or the predicted image, It is not necessary to take a difference between them.

  Further, the inter-layer difference processing unit 522a may not set the difference value in the background area to zero. In this case, the encoder 532a may set the encoded data for difference information in an area other than the feature area to zero. For example, the encoder 532a may set the conversion coefficient after conversion to a frequency component to zero. Note that the motion vector information when the inter-layer difference processing unit 522d performs predictive encoding is supplied to the inter-layer difference processing unit 522a. The inter-layer difference processing unit 522a may calculate a motion vector for a predicted image using the motion vector information supplied from the inter-layer difference processing unit 522d.

  Note that the operations of the inter-layer difference processing unit 522b and the encoder 532b are that the second resolution image is encoded, and the first resolution after encoding by the encoder 532a when the second resolution image is encoded. The operations of the inter-layer difference processing unit 522b and the encoder 532b are substantially the same as the operations of the inter-layer difference processing unit 522a and the encoder 532a, except that the difference from the image may be taken. To do. Similarly, the operations of the inter-layer difference processing unit 522c and the encoder 532c are that the third resolution image is encoded, and when the third resolution image is encoded, after the encoding by the encoder 532b. Except for the fact that a difference from the second resolution image may be obtained, the operation is substantially the same as the operation of the inter-layer difference processing unit 522a and the encoder 532a, and thus the description thereof is omitted.

  As described above, the image quality conversion unit 510 generates, from each of the plurality of captured images, a low image quality image with a low image quality and a feature region image with higher image quality than the low image quality at least in the feature region. Then, the difference processing unit 520 generates a feature region difference image indicating a difference image between the feature region image in the feature region image and the feature region image in the low-quality image. Then, the encoding unit 530 encodes the feature region difference image and the low quality image.

  In addition, the image quality conversion unit 510 generates a low-quality image with reduced resolution from a plurality of captured images, and the difference processing unit 520 includes a feature region image in the feature region image and a feature region image in the low-quality image. A feature region difference image between the image and the image enlarged is generated. Further, the difference processing unit 520 has a spatial frequency component in which the difference between the feature region image and the enlarged image in the feature region is converted into the spatial frequency region, and the data amount of the spatial frequency component is in the region other than the feature region. A reduced feature area difference image is generated.

  As described above, the compressing unit 240 encodes hierarchically by encoding image differences between a plurality of layers having different resolutions. As is clear from this, a part of the compression method by the compression unit 240 of this configuration is H.264. It is clear that a compression scheme according to H.264 / SVC is included. Note that when the image processing apparatus 170 decompresses such a layered compressed moving image, the moving image data of each layer is decoded, and the difference is taken for the region encoded by the inter-layer difference. The captured image having the original resolution can be generated by the addition process with the captured image decoded in the hierarchy.

  Note that the compression unit 340 described with reference to FIG. 4 may have the same components as the compression unit 240 described with reference to FIG. 3 or FIG. For example, the compression processing unit 236 or the image quality conversion unit 510 can adjust the image quality in each of a plurality of feature regions and regions other than the feature regions.

  FIG. 6 shows an example of the feature area information stored in the image DB 175 in a table format. The image DB 175 stores coordinate data indicating the position of the feature region in association with the frame ID. The frame ID may be information for identifying each of a plurality of captured images included as a moving image constituent image in the moving image stream.

  Specifically, the image DB 175 has, as coordinate data indicating the position of the feature area, coordinate data indicating the position of the person's head area, coordinate data indicating the position of the person's body area, and the area of the moving object. Coordinate data indicating the position of is stored. In this figure, an example of coordinate data in the case where the feature region is rectangular is shown. Specifically, the image DB 175 stores the coordinates of vertices located diagonally in a rectangular feature region as coordinate data.

  Thus, the image DB 175 stores the captured image in association with the information indicating the feature region in the captured image. Further, the image DB 175 stores a plurality of captured images in association with information indicating each of a plurality of feature regions in each of the plurality of captured images. For this reason, the image processing apparatus 170 can easily specify the position of a specific type of feature region.

  FIG. 7 shows an example of the target value of the code amount to be reduced by the compression unit 340 in a table format. The compression unit 340 stores the ratio of the code amount to be reduced in each of the plurality of feature regions and regions other than the feature regions in association with Δ indicating a value obtained by subtracting the allowable data amount from the output data amount. . The output data amount may be, for example, a data amount per unit time that is output when a moving image stored in the image DB 175 is output. The allowable data amount is, for example, the data amount per unit time that can be output to the communication network 110 or the display device 180, and may be an example of the allowable output amount in the present invention. In the following description, a region other than the feature region may be referred to as a non-feature region.

  The compression determination unit 360 determines that the captured image should be further compressed when the output data amount exceeds the allowable data amount. At this time, the compression unit 340 further compresses the captured image so as to reduce the code amount of the captured image by the magnitude of Δ.

  At this time, when Δ is larger than 0 and smaller than α1, the compression unit 340 reduces the code amount of 5% of the total reduction amount, which is the total code amount to be reduced, from the region of the moving body, out of the total reduction amount. The goal is to reduce the code amount of 95% of the non-feature region. Further, when Δ is equal to or larger than α1 and smaller than α2, the compression unit 340 obtains a code amount of 5% of the total reduction amount from a region of the body part, and a code amount of 10% of the total reduction amount. The goal is to reduce the code amount of 85% of the total reduction amount from the non-feature region from the region of the moving object. Further, when Δ is greater than or equal to α2 and smaller than α3, the compression unit 340 obtains a code amount of 5% of the total reduction amount from the head region and a code amount of 10% of the total reduction amount. The goal is to reduce the code amount of 15% of the total reduction amount from the body region and the code amount of 70% of the total reduction amount from the non-feature region.

  As described above, the compression unit 340 stores the amount of code to be reduced in association with the type of feature in the feature region and the difference between the output data amount and the output data amount. Then, the compression unit 340 reduces the code amount by the amount stored in association with the type of the feature region and the difference between the output data amount and the output data amount for each of the plurality of feature regions. Each of the images of the plurality of feature areas is compressed. In addition, the compression unit 340 stores the amount of code amount to be reduced in the region other than the feature region in association with the difference between the output data amount and the output data amount. Then, the compression unit 340 compresses the region other than the feature region in the captured image so as to reduce the code amount in the region other than the feature region by the amount stored in association with the difference between the output data amount and the output data amount. To do.

  In addition, when the compression determination unit 360 determines that the captured image should be further compressed, the compression unit 340 selects the non-feature region in preference to the feature region, and compresses the selected region. In addition, the compression unit 340 selects the body region more preferentially than the head region, and selects the moving body region more preferentially than the body region, and compresses the selected region. As described above, the compression unit 340 selects each of a plurality of feature regions having different feature types in the order of priority determined in advance according to the feature types, and compresses the selected regions.

  In addition to Δ, the compression unit 340 may store the ratio of the code amount to be reduced in association with at least one of the storage period, the remaining storage capacity, and the viewing authority. Further, the compression unit 340 may store the ratio of the code amount to be reduced in association with any combination of Δ, the storage period, the remaining storage capacity, and the viewing authority. And the compression part 340 can each compress the image of a some feature area and a non-feature area based on the ratio of the code amount memorize | stored similarly to the operation | movement demonstrated in relation to this figure.

  FIG. 8 shows an example of a compression method that the compression unit 340 compresses. The compression unit 340 stores a plurality of different compression methods in association with the priority with which the compression method is selected. The compression method by the compression unit 340 includes a high-frequency component reduction indicating a compression method for reducing a high-frequency component of spatial frequency, a gradation number reduction indicating a compression method for reducing the number of gradations, and a color indicating a compression method for reducing the number of colors. The number reduction, the frame rate reduction indicating the compression method for reducing the frame rate, and the resolution reduction indicating the compression method for reducing the resolution can be exemplified.

  The compression unit 340 reduces the high frequency component, the number of gradations, the number of colors, the frame rate, and the resolution according to the amount of code to be reduced for each of the plurality of feature regions and non-feature regions. Select the compression method in this order. For example, when compressing an image of each region, the compression unit 340 compresses the image by reducing the high frequency component if the amount of code to be reduced can be reduced by reducing the high frequency component. In addition, the compression unit 340 cannot reduce the amount of code to be reduced only by reducing the high-frequency component, and can further reduce the amount of code to be reduced by reducing the number of gradations. Compression is performed by reducing the high frequency component and the number of gradations. As described above, the compression unit 340 selects a compression method according to the amount of code amount to be reduced for each of the plurality of feature regions and the non-feature region, and uses the selected compression method to select a plurality of feature regions and The image of each non-feature area is compressed.

  It should be noted that an upper limit value of the code amount that can be reduced may be determined in advance for each compression method of high-frequency component reduction, gradation number reduction, color number reduction, frame rate reduction, and resolution reduction. Then, the compression unit 340 may select one or more compression methods that can reduce the code amount by the amount to be reduced based on the upper limit value and the priority. Moreover, the priority of the compression method demonstrated in this figure may be defined for every kind of feature of a feature area. Then, the compression unit 340 may select a compression method with a priority determined for each feature type of the feature region for each feature type of the feature region, and may compress the selected feature method using the selected compression method.

  FIG. 9 shows an example of a processing flow compressed by the image processing apparatus 170. In the example of this figure, an example of the processing flow of the image processing apparatus 170 when the display apparatus 180 is requested to transmit a moving image is shown. Note that the image processing apparatus 170 acquires a user ID and password through the display device 180 from a user who views the moving image using the display device 180 before transmitting the moving image, and uses the acquired user ID and password. Authenticate.

  Then, the authority acquisition unit 384 acquires the user ID, and the output allowable amount acquisition unit 383 acquires the processing allowable amount that can be processed by the display device 180 (S902). The output allowable amount acquisition unit 283 may acquire the processing allowable amount by feedback from the display device 180, or may acquire a processing allowable amount that is predetermined according to the display device 180.

  Then, the image acquisition unit 301 acquires 1 GOP moving image data from the image DB 175 and reads out the positions of the feature regions in a plurality of captured images included in the moving image data from the image DB 175 (S904). Then, the output allowable amount acquisition unit 383 acquires the network bandwidth of the communication network 110 (S906). As described above, the allowable output amount acquisition unit 383 has a predetermined bandwidth as a bandwidth that can be transmitted to the display device 180, a bandwidth of the communication network 110 that is allowed to be used, or Based on the amount of congestion in the communication network 110, the network bandwidth can be determined.

  Then, the compression determination unit 360 determines the allowable data amount based on the network bandwidth acquired in S906 and the processing allowable amount acquired in S902. For example, the smaller network bandwidth and processing allowance may be used as the allowable data amount. Then, the compression determination unit 360 compares the allowable data amount and the output data amount to determine whether or not to further compress (S907).

  If the compression determination unit 360 determines that further compression is to be performed in S907, the compression unit 340 determines a target code amount in each of the plurality of feature regions and the non-feature region (S908). Note that the compression unit 340 uses the value of Δ described in relation to FIG. 7, the ratio of the code amount to be reduced described in relation to FIG. 7, and the code amount in each of a plurality of feature regions and non-feature regions. The target code amount in each of the plurality of feature regions and the non-feature region can be determined.

  Then, the compression unit 340 compresses the captured image included in the moving image data acquired in S904 so that the code amount in each of the plurality of feature regions and the non-feature region becomes the target code amount determined in S908 (S910). Then, the image processing apparatus 170 transmits the moving image data compressed in S910 (S912). If the compression determination unit 360 determines in step S907 that compression is not performed further, the image processing apparatus 170 can transmit the moving image data acquired in step S904.

  Then, the image processing apparatus 170 determines whether or not the last GOP in the moving image to be transmitted has been processed (S914). If the last GOP has not been processed, the process returns to S904. If the last GOP has been processed, the process ends. In the description of this figure, the operation of the image processing apparatus 170 has been described by taking as an example a case where moving image data is processed and transmitted for each GOP for the purpose of easily explaining the operation of the image processing apparatus 170. However, it goes without saying that the image processing apparatus 170 can process moving image data in an arbitrary unit, not limited to 1 GOP. As described above, according to the image processing system 10, it is possible to provide a moving image with an appropriate code amount according to the bandwidth of the communication network 110 or the processing speed that can be processed by the display device 180.

  Note that an example of the processing flow of the image processing apparatus 170 has been described with reference to this figure, taking as an example the case of transmitting a moving image to the display apparatus 180. In addition, the image processing apparatus 170 periodically acquires the remaining storage capacity of the image DB 175 at predetermined time intervals, and stores the remaining storage capacity in the image DB 175 when the acquired remaining storage capacity is smaller than a predetermined value. You can further compress the moving image. Further, the image processing apparatus 170 acquires the storage period of the moving image stored in the image DB 175 periodically at a predetermined time interval, and when the acquired storage period is larger than a predetermined value, the image processing apparatus 170 stores the storage period of the moving image. The stored moving image can be further compressed. For this reason, according to the image processing system 10, it may be possible to prevent the usage efficiency of the image DB 175 from being significantly reduced.

  FIG. 10 shows an example of an image processing system 20 according to another embodiment. The configuration of the image processing system 20 in the present embodiment is the image described with reference to FIG. 1 except that the imaging devices 100a-d have image processing units 804a-d (hereinafter collectively referred to as image processing units 804). The configuration of the processing system 10 is the same.

  The image processing unit 804 has components other than the image acquisition unit 250 among the components included in the image processing apparatus 120. The function and operation of each component included in the image processing unit 804 is changed to that each component included in the image processing device 120 processes a moving image obtained by the decompression processing by the compressed moving image decompression unit 202. The functions and operations of each component included in the image processing apparatus 120 may be substantially the same except that the moving image captured by the imaging unit 102 is processed. Also in the image processing system 20 having such a configuration, the same effects as those described in relation to the image processing system 10 in FIGS. 1 to 9 can be obtained.

  Note that the image processing unit 804 acquires a moving image including a plurality of captured images expressed in the RAW format from the imaging unit 102, and converts the plurality of captured images expressed in the RAW format included in the acquired moving image in the RAW format. It may be compressed as it is. Note that the image processing unit 804 may detect one or more feature regions from a plurality of captured images expressed in the RAW format. The image processing unit 804 may compress a moving image including a plurality of captured images in the compressed RAW format. Note that the image processing unit 804 can compress the moving image by the compression method described as the operation of the image processing apparatus 120 with reference to FIGS. Further, the image processing apparatus 170 can acquire a plurality of captured images expressed in the RAW format by expanding the moving image acquired from the image processing unit 804. The image processing apparatus 170 enlarges each of the plurality of captured images expressed in the RAW format acquired by decompression for each region, and performs synchronization processing for each region. At this time, the image processing apparatus 170 may perform synchronization processing with higher accuracy in the feature region than in the region other than the feature region.

  Note that the image processing apparatus 170 may perform super-resolution processing on the image of the feature region in the captured image obtained by the synchronization processing. Super-resolution processing in the image processing apparatus 170 includes super-resolution processing based on principal component analysis as described in JP-A-2006-350498, or subject as described in JP-A-2004-88615. The super-resolution processing based on the movement of the can be illustrated.

  Note that the image processing apparatus 170 may perform super-resolution processing for each object included in the feature region. For example, when the feature region includes a human face image, the image processing apparatus 170 performs super-resolution processing for each face part (for example, eyes, nose, mouth, etc.) as an example of the object. In this case, the image processing apparatus 170 stores learning data such as a model described in Japanese Patent Application Laid-Open No. 2006-350498 for each face part (for example, eyes, nose, mouth). Then, the image processing apparatus 170 may perform super-resolution processing on the image of each face part using the learning data selected for each face part included in the feature region.

  In this way, the image processing apparatus 170 can reconstruct the image of the feature region using principal component analysis (PCA). As an image reconstruction method by the image processing apparatus 170 and a learning method for the image reconstruction, in addition to learning and image reconstruction by principal component analysis (PCA), local preserving projection (LPP) , Linear discriminant analysis (LDA), independent component analysis (ICA), multidimensional scaling (MDS), support vector machine (support vector regression), neural network, hidden Markov model, Bay Inference, maximum posterior probability estimation, iterative backprojection, Wavelet transform, locally linear embedding (locally linea) Techniques such as reembedding (LLE) and Markov random field (MRF) can be used.

  In addition to the model as described in Japanese Patent Application Laid-Open No. 2006-350498, the learning data includes low frequency components and high frequency components of the object image respectively extracted from a large number of sample images of the object. Good. Here, by clustering the low frequency components of the object image for each of the plurality of object types by the K-means method or the like, the low frequency components of the object image in each of the plurality of object types are converted into a plurality of clusters. It may be clustered. Moreover, a typical low frequency component (for example, a centroid value) may be determined for each cluster.

  Then, the image processing device 170 extracts a low frequency component from the image of the object included in the feature region in the captured image. Then, the image processing apparatus 170 determines, as a representative low-frequency component, a value that matches the extracted low-frequency component among the low-frequency component clusters extracted from the sample image of the extracted object type object. Identify the cluster. Then, the image processing apparatus 170 identifies a cluster of high frequency components associated with the low frequency component included in the identified cluster. In this way, the image processing apparatus 170 can specify a cluster of high-frequency components that are correlated with the low-frequency components extracted from the objects included in the captured image. Then, the image processing apparatus 170 may convert the image of the object into a high-quality image with higher image quality using high-frequency components that represent the specified cluster of high-frequency components. For example, the image processing apparatus 170 may add the high-frequency component selected for each object with a weight according to the distance from the center of each object to the processing target position on the face to the object image. Note that the representative high-frequency component may be generated by closed-loop learning. As described above, the image processing apparatus 170 selects and uses desired learning data for each object from learning data generated by learning for each object. There are cases where image quality can be improved.

  Note that in super-resolution processing based on principal component analysis as described in JP-A-2006-350498, an image of an object is represented by a principal component vector and a weighting coefficient. The data amounts of these weighting coefficients and principal component vectors are significantly smaller than the data amount of pixel data included in the object image itself. Therefore, the image processing unit 804 may calculate the weighting coefficient from the image of the object included in the feature area in the compression processing for compressing the image of the feature area in the plurality of captured images acquired from the imaging unit 102. In other words, the image processing unit 804 can compress the image of the object included in the feature region by representing the principal component vector and the weighting coefficient. Then, the image processing unit 804 may transmit the principal component vector and the weighting coefficient to the image processing apparatus 170. In this case, the image processing apparatus 170 can reconstruct an image of an object included in the feature region using the principal component vector and the weighting coefficient acquired from the image processing unit 804. Note that the image processing unit 804 is included in the feature region using a model that represents an object with various feature parameters in addition to a model based on principal component analysis as described in JP-A-2006-350498. Needless to say, an image of an object can be compressed. Note that, also in the configuration of the image processing system 10 described with reference to FIGS. 1 to 9, the image processing apparatus 170 can perform the above-described super-resolution processing on the image of the feature region. In the image processing system 10 and the image processing system 20, the compression unit 340 may further compress the captured image by representing the image with a principal component vector and a weighting coefficient, as in the image processing apparatus 120 described above. it can.

  FIG. 11 shows an example of a hardware configuration of an electronic information processing apparatus 1500 such as a computer that functions as the image processing apparatus 120 and the image processing apparatus 170. The electronic information processing apparatus 1500 includes a CPU peripheral part, an input / output part, and a legacy input / output part. The CPU peripheral section includes a CPU 1505, a RAM 1520, a graphic controller 1575, and a display device 1580 that are connected to each other by a host controller 1582. The input / output unit includes a communication interface 1530, a hard disk drive 1540, and a CD-ROM drive 1560 that are connected to the host controller 1582 by the input / output controller 1584. The legacy input / output unit includes a ROM 1510, a flexible disk drive 1550, and an input / output chip 1570 connected to the input / output controller 1584.

  The host controller 1582 connects the RAM 1520, the CPU 1505 that accesses the RAM 1520 at a higher transfer rate, and the graphic controller 1575. The CPU 1505 operates according to the contents of programs stored in the ROM 1510 and the RAM 1520 to control each unit. The graphic controller 1575 acquires image data generated by the CPU 1505 or the like on a frame buffer provided in the RAM 1520 and displays the image data on the display device 1580. Alternatively, the graphic controller 1575 may include a frame buffer that stores image data generated by the CPU 1505 or the like.

  The input / output controller 1584 connects the host controller 1582 to the hard disk drive 1540, the communication interface 1530, and the CD-ROM drive 1560, which are relatively high-speed input / output devices. The hard disk drive 1540 stores programs and data used by the CPU 1505. The communication interface 1530 is connected to the network communication device 1598 to transmit / receive programs or data. The CD-ROM drive 1560 reads a program or data from the CD-ROM 1595 and provides it to the hard disk drive 1540 and the communication interface 1530 via the RAM 1520.

  The input / output controller 1584 is connected to the ROM 1510, the flexible disk drive 1550, and the relatively low-speed input / output device of the input / output chip 1570. The ROM 1510 stores a boot program that is executed when the electronic information processing apparatus 1500 is started, a program that depends on the hardware of the electronic information processing apparatus 1500, and the like. The flexible disk drive 1550 reads a program or data from the flexible disk 1590 and provides it to the hard disk drive 1540 and the communication interface 1530 via the RAM 1520. The input / output chip 1570 connects various input / output devices via the flexible disk drive 1550 or a parallel port, serial port, keyboard port, mouse port, and the like.

  A program executed by the CPU 1505 is stored in a recording medium such as the flexible disk 1590, the CD-ROM 1595, or an IC card and provided by the user. The program stored in the recording medium may be compressed or uncompressed. The program is installed in the hard disk drive 1540 from the recording medium, read into the RAM 1520, and executed by the CPU 1505. The program executed by the CPU 1505 includes the electronic information processing apparatus 1500 in each component included in the image processing apparatus 120 described with reference to FIGS. 1 to 10 or the image processing described with reference to FIGS. It is made to function as each component which the apparatus 170 has.

  The program shown above may be stored in an external storage medium. As the storage medium, in addition to the flexible disk 1590 and the CD-ROM 1595, an optical recording medium such as a DVD or PD, a magneto-optical recording medium such as an MD, a tape medium, a semiconductor memory such as an IC card, or the like can be used. Further, a storage device such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet may be used as a recording medium and provided to the electronic information processing apparatus 1500 as a program via the network. As described above, the electronic information processing apparatus 1500 controlled by the program functions as the image processing apparatus 120 and the image processing apparatus 170.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the above-described embodiment. It is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

1 is a diagram illustrating an example of an image processing system 10 according to an embodiment. 2 is a diagram illustrating an example of a block configuration of an image processing apparatus 120. FIG. It is a figure which shows an example of the block configuration of the compression part 240. FIG. 2 is a diagram illustrating an example of a block configuration of an image processing apparatus 170. FIG. It is a figure which shows an example of the other block structure of the compression part. It is a figure which shows an example of the characteristic area information which image DB175 has memorize | stored in a table format. It is a figure which shows an example of the target value of the code amount which should be reduced by the compression part 340 in a table format. It is a figure which shows an example of the compression method which the compression part 340 compresses. 6 is a diagram illustrating an example of a processing flow compressed by the image processing apparatus 170. FIG. It is a figure which shows an example of the image processing system 20 which concerns on other embodiment. 2 shows an example of a hardware configuration of an electronic information processing apparatus 1500 that functions as an image processing apparatus 120 and an image processing apparatus 170.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Image processing system 20 Image processing system 100 Image pick-up device 102 Image pick-up part 104 Image pick-up image compression part 110 Communication network 120 Image processing device 130 Person 140 Mobile body 150 Monitoring object space 160 Space 165 Space 170 Image processing device 175 Image DB
180 Display Device 201 Compressed Movie Acquisition Unit 202 Compressed Movie Expansion Unit 203 Feature Area Detection Unit 206 Association Processing Unit 207 Output Unit 210 Compression Control Unit 240 Compression Unit 232 Image Dividing Unit 234 Fixed Value Unit 236 Compression Processing Unit 250 Image Acquisition Unit 260 compression determination unit 265 condition determination unit 270 condition acquisition unit 283 output allowable amount acquisition unit 301 image acquisition unit 302 association analysis unit 310 expansion control unit 320 expansion unit 322 decoder 330 synthesis unit 340 compression unit 360 compression determination unit 365 condition determination Unit 370 condition acquisition unit 380 determination information acquisition unit 381 storage period acquisition unit 382 remaining storage amount acquisition unit 383 output allowable amount acquisition unit 384 authority acquisition unit 510 image quality conversion unit 520 difference processing unit 522 inter-layer difference processing unit 530 encoding unit 532 Encoder 804 Image processing unit 1500 Electronic information processing apparatus 150 CPU
1510 ROM
1520 RAM
1530 Communication interface 1540 Hard disk drive 1550 Flexible disk drive 1560 CD-ROM drive 1570 Input / output chip 1575 Graphic controller 1580 Display device 1582 Host controller 1584 Input / output controller 1590 Flexible disk 1595 CD-ROM
1598 Network communication device

Claims (26)

A storage unit for storing images;
A compression determination unit for determining whether or not to compress the image;
A compression unit that compresses the image with a different compression strength between a feature region in the image and a region other than the feature region in the image when the compression determination unit determines that the image should be compressed; Image processing system. The storage unit stores a compressed image,
The compression determination unit determines whether or not the image should be further compressed;
When the compression determination unit determines that the image should be further compressed, the compression unit compresses the image with a different compression strength between a feature region in the image and a region other than the feature region in the image. The image processing system according to claim 1. 3. The compression unit according to claim 2, wherein, when the compression determination unit determines that the image should be further compressed, the compression unit compresses the region other than the feature region in the image with a stronger compression strength than the feature region in the image. The image processing system described. The storage unit stores the image in association with information indicating a feature region in the image,
The compression unit is stronger than the feature region indicated by the information stored in the storage unit in association with the image in a region other than the feature region indicated by the information stored in the storage unit in association with the image. The image processing system according to claim 3, wherein the image processing system is compressed with a compression strength. The storage unit stores a compressed moving image including a plurality of moving image constituent images that are the images in association with information indicating a feature region in the moving image,
The compression unit is configured to store the moving image stored in the storage unit in a region other than the feature region in the moving image indicated by the information stored in the storage unit in association with the moving image. The image processing system according to claim 4, wherein compression is performed with a stronger compression strength than a feature area in the moving image indicated by information stored in association with the image. The compression unit corresponds to the moving image in a region other than the feature region in the moving image indicated by the information stored in the storage unit in association with the moving image. The image processing system according to claim 5, wherein compression is performed with a compression strength stronger than a feature area in the moving image indicated by the stored information. The storage unit stores the plurality of moving image constituent images in association with information indicating a feature region in each of the plurality of moving image constituent images.
The compression unit is configured to associate each of the plurality of moving image constituent images with each of the plurality of moving image constituent images, except for the feature region in each of the plurality of moving image constituent images indicated by the information stored in the storage unit The image processing system according to claim 6, wherein the area is compressed with a compression strength stronger than a characteristic area in each of the plurality of moving image constituent images. The storage unit stores the plurality of moving image constituent images in association with information indicating each of a plurality of feature regions in each of the plurality of moving image constituent images.
The image processing system according to claim 7, wherein the compression unit compresses each of the plurality of feature regions in each of the plurality of moving image constituent images with a compression strength corresponding to a feature type of the feature region. 9. The compression unit according to claim 8, wherein the compression unit compresses the image by reducing the image quality of each of the plurality of feature regions in each of the plurality of moving image constituent images to an image quality corresponding to a feature type of the feature region. The image processing system described. The storage unit stores, in association with information indicating each of the plurality of feature regions, the plurality of moving image constituent images compressed with a higher compression strength than regions other than the feature regions in each of the plurality of feature regions. And
The image processing system according to claim 9, wherein the compression unit compresses the image by further reducing image quality in a region other than the feature region than image quality in the plurality of feature regions. The storage unit associates information indicating each of the plurality of feature regions with the plurality of moving image constituent images compressed with a compression strength corresponding to a feature type of the feature region in each of the plurality of feature regions. Remember,
The compression unit compresses the image by reducing the image quality of each of the plurality of feature regions in each of the plurality of moving image constituent images by a reduction amount corresponding to the feature type of the feature region. The image processing system described in 1. A condition determination unit for determining whether a condition to be satisfied when the compression determination unit determines that the image should be further compressed is satisfied;
The image processing system according to claim 11, wherein the compression determination unit determines that the image should be further compressed when the condition determination unit determines that the condition is satisfied. The condition determination unit determines whether or not a condition indicating that a remaining storage capacity that can be further stored in the storage unit is smaller than a predetermined value is satisfied,
The image processing system according to claim 12, wherein the compression determination unit determines that the image should be further compressed when the condition determination unit determines that the remaining storage capacity satisfies the condition. The condition determining unit determines whether or not a condition indicating that a time length of a period in which the storage unit stores the image is longer than a predetermined value is satisfied;
The compression determination unit determines that the image should be further compressed when the condition determination unit determines that the time length of the period in which the storage unit stores the image satisfies the condition. The image processing system according to claim 12. The condition determination unit determines whether a condition indicating that an output allowable amount that can be output to an output destination to which the image is output is smaller than a predetermined value is satisfied,
The image processing system according to claim 12, wherein the compression determination unit determines that the image should be further compressed when the condition determination unit determines that the allowable output amount satisfies the condition. The condition determining unit determines whether or not a condition indicating that a viewing authority for browsing the image is smaller than a predetermined value is satisfied,
The image processing system according to claim 12, wherein the compression determination unit determines that the image should be further compressed when the condition determination unit determines that the viewing authority satisfies the condition. The compression unit compresses the image by reducing the image quality of each of the plurality of feature regions in each of the plurality of moving image constituent images to an image quality according to the feature type of the feature region and the viewing authority. The image processing system according to claim 16. A condition acquisition unit for acquiring the condition;
The image processing according to claim 12, wherein the condition determination unit determines that the image should be further compressed when the condition determination unit determines that the condition acquired by the condition acquisition unit is satisfied. system. The image processing system according to claim 11, wherein the compression unit compresses the image by further reducing resolution in a region other than the feature region than resolution in the plurality of feature regions. The image processing system according to claim 11, wherein the compression unit compresses the image by further reducing the number of colors in a region other than the feature region than the number of colors in the plurality of feature regions. The image processing system according to claim 11, wherein the compression unit compresses the image by further reducing the number of gradations in a region other than the feature region than the number of gradations in the plurality of feature regions. The image processing system according to claim 11, wherein the compression unit compresses the image by further reducing a code amount in a region other than the feature region than a code amount in the plurality of feature regions. The image processing system according to claim 22, wherein the compression unit compresses the image by further reducing frequency components in a higher frequency region in a region other than the feature region than the plurality of feature regions. The storage unit stores a plurality of moving images each including a plurality of moving image constituent images that are the images,
The compression determination unit determines whether or not to compress each of the plurality of moving image constituent images included in the plurality of moving images,
The compression unit, when the compression determination unit determines that at least one moving image constituent image should be compressed, at least one of the plurality of feature regions in the moving image constituent image included in the at least one moving image. The image processing system according to claim 1, wherein the compression determination unit performs compression with an intensity corresponding to a determination result determined for each of the plurality of moving image constituent images. A storage stage for storing images;
Compression determination step for determining whether or not to compress the image;
A compression step of compressing the image with a different compression strength between a feature region in the image and a region other than the feature region in the image when it is determined in the compression determination step that the image should be compressed. An image processing method provided. A program for an image processing system, comprising:
A storage unit for storing images,
A compression determination unit for determining whether or not to compress the image;
When the compression determination unit determines that the image is to be compressed, the compression determination unit functions as a compression unit that compresses the image with a different compression strength between a feature region in the image and a region other than the feature region in the image. program.

Images